CN112069228A - Event sequence-oriented cause and effect visualization method and device - Google Patents
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Abstract
The invention discloses a causal visualization method and a causal visualization device for an event sequence, wherein the causal visualization method comprises the following steps: inputting a causal relationship and an event sequence conforming to the causal relationship, and generating a layout of event nodes by using the existing causal network visualization; counting event information according to the event sequence, displaying the event information through icon visualization, and placing a generated event icon on a corresponding event layout in the causal network visualization, wherein the event information comprises the category of an event, the time distribution of the event occurrence and the frequency of the event occurrence; excavating frequently-occurring event occurrence modes in the event sequence by using a frequent mode excavating algorithm, and visualizing the excavated event occurrence modes in a time line form; for each event occurrence mode, generating a causal event stream according to the causal relationship of the events in the causal network, and displaying the development sequence of the events in time and the causal relationship among the events; a visualization of all event sequences is generated in the form of a timeline.
Description
Technical Field
The invention relates to the field of computer visualization, in particular to a causal visualization method and device for an event sequence.
Background
Causal analysis of event sequence data can characterize the relationships between events and can play an important role in various fields, such as marketing behavior analysis, electronic medical record and healthcare analysis, and error log analysis, among others. Control experiments are a common method for deducing the cause of an event, but due to the high cost of the experimental setup, control experiments are not suitable for the application field in many cases. Based on this, experts have invented a series of causal detection algorithms to automatically infer the causal relationships contained in the observed data. Visualization has also been applied to causal analysis due to the effectiveness of the analysis. Researchers have invented a collection of visualizations suitable for revealing causal networks. However, these visualizations are only applicable to causal networks that present static tabular data, and cannot present causal networks in time-information-bearing event sequences. Existing event visualization methods, on the other hand, typically use a visual representation based on a timeline to visualize the event sequence. However, displaying causal information of events to explain the occurrence of events while preserving the temporal order of the events in the visualization has not been addressed. The current event visualization and cause and effect visualization do not relate to the visualization of the cause and effect relationship of the event, and a specific theory and visualization method is lacked.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method and an apparatus for event sequence-oriented cause and effect visualization, so as to solve a problem that the occurrence of an event is still unsolved by displaying cause and effect relationship information of the event while preserving a time sequence of the event in the visualization.
In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows:
in a first aspect, a method for causal visualization of an event sequence includes:
inputting a causal relationship and an event sequence conforming to the causal relationship, and generating a layout of event nodes by using the existing causal network visualization;
counting event information according to the event sequence, displaying the event information through icon visualization, and placing a generated event icon on a corresponding event layout in the causal network visualization, wherein the event information comprises the category of an event, the time distribution of the event occurrence and the frequency of the event occurrence;
excavating frequently-occurring event occurrence modes in the event sequence by using a frequent mode excavating algorithm, and visualizing the excavated event occurrence modes in a time line form;
for each event occurrence mode, generating a causal event stream according to the causal relationship of the events in the causal network, and displaying the development sequence of the events in time and the causal relationship among the events;
a visualization of all event sequences is generated in the form of a timeline.
In a second aspect, an embodiment of the present invention further provides an event sequence-oriented cause and effect visualization apparatus, including:
the input visualization module is used for inputting the causal relationship and the event sequence conforming to the causal relationship, and generating the layout of the event nodes by using the existing causal network visualization;
the causal graph module is used for counting event information according to the event sequence, displaying the event information through icon visualization, and placing the generated event icons on corresponding event layouts in the causal network visualization, wherein the event information comprises event types, event occurrence time distribution and event occurrence frequency;
the sequence mode module is used for excavating frequently-occurring event occurrence modes in the event sequence by using a frequent mode excavating algorithm and visually excavating the obtained event occurrence modes in a time line form;
the causal flow module is used for generating a causal event flow according to the causal relationship of the events in the causal network for each event occurrence mode, and showing the development sequence of the events in time and the causal relationship among the events;
and the sequence detail module is used for generating the visualization of all event sequences in a time line mode.
In a third aspect, an embodiment of the present invention further provides a cause and effect visualization method for an event sequence, which is applied to cause and effect visualization of a table tennis motion event sequence, and includes:
inputting a causal relationship between table tennis technologies and a table tennis technology sequence conforming to the causal relationship, and visually generating a layout about the causal relationship of the table tennis technology by using an existing causal network;
counting event information according to a ping-pong skill sequence, displaying the event information through icon visualization, representing event categories by using colors of icons, representing frequency by using radian of outer-layer rings, representing time distribution by using pie charts in the icons, and placing the generated event icons on corresponding event layouts in causal network visualization, wherein the event information comprises technology categories, time distribution of technology use and frequency of technology use;
excavating technology use patterns frequently appearing in a table tennis technology sequence by using a frequent pattern excavation algorithm, and visually excavating the obtained table tennis technology use patterns in a time line form;
for each table tennis technique use mode, generating a causal event stream according to the causal relationship of the events in the causal network, and showing the time development sequence of the table tennis technique and the causal relationship between the techniques;
a visualization of all ping-pong skill sequences is generated in the form of a timeline.
According to the technical scheme, the method has the advantages that the layout method based on force guidance is used, common display of event time information and cause and effect information is achieved, overlapping and crossing on the layout are avoided, cause and effect relationships and time sequences are clearly identified, and readability is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a method for event sequence-oriented cause and effect visualization according to an embodiment of the present invention;
FIG. 2 is an overview of a sequence of event-oriented cause and effect visualization in accordance with an embodiment of the present invention;
FIG. 3 is a visualization of a causal event stream in an embodiment of the present invention;
FIG. 4 is a causal structure extracted in an embodiment of the present invention;
fig. 5 is a block diagram of a cause and effect visualization apparatus for event sequences according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example 1:
fig. 1 is a flowchart of a method for event sequence-oriented cause and effect visualization according to an embodiment of the present invention; the method for visualizing the cause and effect of the event sequence comprises the following steps:
s101, inputting a causal relationship and an event sequence conforming to the causal relationship, and generating a layout of event nodes by using the existing causal network visualization;
specifically, N causal networks G are input, each node in G representing an event, and different causal networks G share a set of events. Each causal network G comprises a plurality of event sequences S.
Step S102, counting event information according to an event sequence, displaying the event information through icon visualization, and placing a generated event icon on a corresponding event layout in the causal network visualization, wherein the event information comprises event types, event occurrence time distribution and event occurrence frequency;
the event information statistics according to the event sequence specifically includes:
category of statistical events: classifying the event data with the metadata according to the existing category; for data where metadata does not exist, each event becomes a class separately; representing the event category using a color of the icon;
counting the frequency of events: counting the occurrence frequency of events in all event sequences, wherein the radian of an outer ring is used in an icon to represent the occurrence frequency, and the larger the radian is, the higher the occurrence frequency of the events is;
time distribution of statistical events: dividing each event sequence into equal parts according to the time sequence of 4, counting the occurrence frequency of the events on each part, and forming time analysis; non-statistics that the sequence length exceeds 4; the time distribution is represented by a pie chart within the icon, which shows the frequency of occurrence of the events in parts 1-4 in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
Step S103, excavating frequent event occurrence modes in the event sequence by using a frequent pattern excavating algorithm, and visualizing the excavated event occurrence modes in a time line form;
and mining to obtain an event subsequence with the occurrence frequency of more than 50% as a frequent event mode.
Step S104, for each event occurrence mode, generating a causal event stream according to the causal relationship of the events in the causal network, and showing the development sequence of the events in time and the causal relationship among the events, specifically comprising the following sub-steps:
step S1041, counting causal relationships involved in the event sequence: according to the events in the event sequence, filtering out irrelevant causal relationships in the causal network and generating a causal subnetwork corresponding to the event sequence; the irrelevant definition is that no directed edge points to any event in the event sequence on the path (path) of the causal relationship;
step S1042, obtaining a cause and effect structure: combining the causal relationships obtained by filtering to obtain causal structures which are respectively a Chain structure, a Fork structure and a V structure;
step S1043, obtaining a topological order of events in the event sequence: acquiring topological sequencing of events in the event sequence, and optionally selecting sequencing of events serving as a parent node in the causal network before events serving as child nodes;
step S1044, visual layout: generating a horizontal axis of the events from left to right according to the occurrence sequence of the events, and then generating a vertical axis of the events from top to bottom according to the topological sorting of the events, wherein the positions on the coordinate axis are all marked with corresponding event names; generating a dot representing an event, the ordinate and the abscissa referring to the position of the event on the coordinate axis; connecting the event dots from left to right; for an event with a parent node in a causal subnetwork, a solid rectangle is placed beside a dot of the event, the solid rectangle is called an event rectangle, and the length of the rectangle represents the number of the parent nodes of the event; connecting the event round dots and the event rectangles in a stream mode to show the causal relationship in the causal subnetwork; adjusting the position of the event rectangle on the transverse axis in a force-guided manner according to the cause and effect structure, so as to ensure the readability of the cause and effect structure; in this visualization, the chronological order of the occurrence of the events can be seen from left to right, and the causal relationship of the occurrence of the events can be seen from top to bottom.
Step S105, a visualization of all event sequences is generated in the form of a timeline.
Specifically, each event is represented by a color of a dot, and the dots are arranged on the horizontal axis to show the occurrence order of the events in the sequence.
Example 2:
the invention is based on a cause and effect visualization method facing an event sequence, and when the method is applied to analyzing a motion event sequence of a table tennis, the method comprises the following steps:
the method comprises the following steps: inputting a cause-and-effect relationship between table tennis techniques and a table tennis technique sequence conforming to the cause-and-effect relationship; the layout of the causal relationships on the table tennis technique is generated using existing causal network visualizations.
Step two: counting event information according to a ping-pong skill sequence, wherein the event information comprises technology category, time distribution of technology use and frequency of technology use, and the information of the three aspects is displayed in a visualized way through icons; representing the event category by using the color of the icon, representing the frequency by the radian of the outer ring, and representing the time distribution by a pie chart in the icon; the generated event icons are placed on corresponding event layouts in the causal network visualization; the resulting causal network visualization is shown in fig. 2 (a).
Step three: excavating frequently-appearing technical use modes in a table tennis technical sequence by using an existing frequent mode excavation algorithm; and visualizing the use mode of the table tennis technique obtained by mining in a time line mode.
Step four: for each ping-pong technique usage pattern, a causal event stream is generated according to the causal relationship of the events in the causal network, as shown in fig. 2(B), showing the development sequence of the ping-pong technique in time and the causal relationship between the techniques.
This step is the core of the present invention and is divided into the following substeps.
1) Statistics of causal relationships involved in ping-pong technical sequences
According to the technology in the table tennis technical sequence, irrelevant cause-and-effect relationships in a cause-and-effect network are filtered out, and a cause-and-effect sub-network corresponding to the table tennis technical sequence is generated; unrelated definition is that the path where the causal relationship exists has no edge pointing to any technique in the ping-pong technique sequence;
2) obtaining cause and effect structure
Combining the causal relationships obtained by filtering to obtain causal structures, which are respectively a Chain structure (representing a Chain causal relationship), a Fork structure (representing a causal relationship of a common ancestor), and a V structure (representing a common determinant causal relationship), as shown in fig. 4;
3) obtaining a topological ordering of events in a sequence of events
The sequence of the events in the topological ordering satisfies that the event as a parent node in the causal network must be arranged before the event as a child node; there are a variety of sequences that meet the criteria, and any sequence that meets the step is taken;
4) visual layout
As shown in fig. 3(a), the horizontal axis is generated from left to right according to the occurrence sequence of the ping-pong technique, and the vertical axis is generated from top to bottom according to the causal topological sorting of the ping-pong technique, as shown in fig. 3 (C). Marking corresponding technical names at the positions on the coordinate axis; as shown in fig. 3(B, F), an event dot representing the technology is generated, and the ordinate and abscissa refer to the position of the technology on the coordinate axis; connecting the event dots from left to right; for the ping-pong technique with parent nodes in the causal subnetwork, as shown in fig. 3(E), a solid rectangle, called an event rectangle, is placed beside the dots, and the length of the rectangle represents the number of parent nodes of the ping-pong technique; as shown in fig. 3(D), event dots and event rectangles are connected in a stream to show causal relationships in a causal subnetwork; adjusting the position of the event rectangle on the horizontal axis in a force-guided manner according to the cause and effect structure in 2), as shown in fig. 4, to ensure the readability of the cause and effect structure; in the visualization, the time sequence of the occurrence of the ping-pong skill can be seen from left to right, and the causal relationship used by the ping-pong skill can be seen from top to bottom;
step five: a visualization of all ping-pong skill sequences is generated in the form of a timeline, as shown in fig. 1 (C).
Example 3:
referring to fig. 5, an event sequence-oriented cause and effect visualization apparatus is further provided in an embodiment of the present invention, and the apparatus may execute any event sequence-oriented cause and effect visualization method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. As shown in fig. 5, includes:
an input visualization module 901, configured to input causal relationships and event sequences that meet the causal relationships, and generate a layout about event nodes using an existing causal network visualization;
a causal graph module 902, configured to count event information according to an event sequence, and display event information through icon visualization, where a generated event icon is placed on a corresponding event layout in the causal network visualization, where the event information includes a category of an event, a time distribution of event occurrence, and a frequency of event occurrence;
a sequence pattern module 903, configured to excavate an event occurrence pattern frequently occurring in the event sequence by using a frequent pattern mining algorithm, and visualize the excavated event occurrence pattern in a timeline form;
a causal flow module 904, configured to, for each event occurrence mode, generate a causal event flow according to the causal relationship of the events in the causal network, and show the temporal development sequence of the events and the causal relationship between the events;
a sequence details module 905 for generating a visualization of all event sequences in the form of a timeline.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described device embodiments are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A method for causal visualization of a sequence of events, comprising:
inputting a causal relationship and an event sequence conforming to the causal relationship, and generating a layout of event nodes by using the existing causal network visualization;
counting event information according to the event sequence, displaying the event information through icon visualization, and placing a generated event icon on a corresponding event layout in the causal network visualization, wherein the event information comprises the category of an event, the time distribution of the event occurrence and the frequency of the event occurrence;
excavating frequently-occurring event occurrence modes in the event sequence by using a frequent mode excavating algorithm, and visualizing the excavated event occurrence modes in a time line form;
for each event occurrence mode, generating a causal event stream according to the causal relationship of the events in the causal network, and displaying the development sequence of the events in time and the causal relationship among the events;
a visualization of all event sequences is generated in the form of a timeline.
2. The method for event sequence-oriented causal visualization according to claim 1, wherein counting event information according to an event sequence specifically includes:
category of statistical events: classifying the event data with the metadata according to the existing category; for data where metadata does not exist, each event becomes a class separately; representing the event category using a color of the icon;
counting the frequency of events: counting the occurrence frequency of events in all event sequences, wherein the radian of an outer ring is used in an icon to represent the occurrence frequency, and the larger the radian is, the higher the occurrence frequency of the events is;
time distribution of statistical events: dividing each event sequence into equal parts according to the time sequence of 4, counting the occurrence frequency of the events on each part, and forming time analysis; non-statistics that the sequence length exceeds 4; the time distribution is represented by a pie chart within the icon, which shows the frequency of occurrence of the events in parts 1-4 in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
3. The method according to claim 1, wherein for each event occurrence mode, a causal event stream is generated according to event causal relationships in a causal network, showing the order of events in time and causal relationships between events, and specifically includes:
statistics of causal relationships involved in the sequence of events: according to the events in the event sequence, filtering out irrelevant causal relationships in the causal network and generating a causal subnetwork corresponding to the event sequence; the irrelevant definition is that no directed edge points to any event in the event sequence on the path (path) of the causal relationship;
acquiring a causal structure: combining the causal relationships obtained by filtering to obtain causal structures which are respectively a Chain structure, a Fork structure and a V structure;
obtaining the topological ordering of events in the event sequence: acquiring topological sequencing of events in the event sequence, and optionally selecting sequencing of events serving as a parent node in the causal network before events serving as child nodes;
visual layout: generating a horizontal axis of the events from left to right according to the occurrence sequence of the events, and then generating a vertical axis of the events from top to bottom according to the topological sorting of the events, wherein the positions on the coordinate axis are all marked with corresponding event names; generating a dot representing an event, the ordinate and the abscissa referring to the position of the event on the coordinate axis; connecting the event dots from left to right; for an event with a parent node in a causal subnetwork, a solid rectangle is placed beside a dot of the event, the solid rectangle is called an event rectangle, and the length of the rectangle represents the number of the parent nodes of the event; connecting the event round dots and the event rectangles in a stream mode to show the causal relationship in the causal subnetwork; adjusting the position of the event rectangle on the transverse axis in a force-guided manner according to the cause and effect structure, so as to ensure the readability of the cause and effect structure; in this visualization, the chronological order of the occurrence of the events can be seen from left to right, and the causal relationship of the occurrence of the events can be seen from top to bottom.
4. A cause and effect visualization device for an event sequence, comprising:
the input visualization module is used for inputting the causal relationship and the event sequence conforming to the causal relationship, and generating the layout of the event nodes by using the existing causal network visualization;
the causal graph module is used for counting event information according to the event sequence, displaying the event information through icon visualization, and placing the generated event icons on corresponding event layouts in the causal network visualization, wherein the event information comprises event types, event occurrence time distribution and event occurrence frequency;
the sequence mode module is used for excavating frequently-occurring event occurrence modes in the event sequence by using a frequent mode excavating algorithm and visually excavating the obtained event occurrence modes in a time line form;
the causal flow module is used for generating a causal event flow according to the causal relationship of the events in the causal network for each event occurrence mode, and showing the development sequence of the events in time and the causal relationship among the events;
and the sequence detail module is used for generating the visualization of all event sequences in a time line mode.
5. The method for event sequence-oriented causal visualization according to claim 1, wherein counting event information according to an event sequence specifically includes:
category of statistical events: classifying the event data with the metadata according to the existing category; for data where metadata does not exist, each event becomes a class separately; representing the event category using a color of the icon;
counting the frequency of events: counting the occurrence frequency of events in all event sequences, wherein the radian of an outer ring is used in an icon to represent the occurrence frequency, and the larger the radian is, the higher the occurrence frequency of the events is;
time distribution of statistical events: dividing each event sequence into equal parts according to the time sequence of 4, counting the occurrence frequency of the events on each part, and forming time analysis; non-statistics that the sequence length exceeds 4; the time distribution is represented by a pie chart within the icon, which shows the frequency of occurrence of the events in parts 1-4 in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
6. The method according to claim 1, wherein for each event occurrence mode, a causal event stream is generated according to event causal relationships in a causal network, showing the order of events in time and causal relationships between events, and specifically includes:
statistics of causal relationships involved in the sequence of events: according to the events in the event sequence, filtering out irrelevant causal relationships in the causal network and generating a causal subnetwork corresponding to the event sequence; the irrelevant definition is that no directed edge points to any event in the event sequence on the path (path) of the causal relationship;
acquiring a causal structure: combining the causal relationships obtained by filtering to obtain causal structures which are respectively a Chain structure, a Fork structure and a V structure;
obtaining the topological ordering of events in the event sequence: acquiring topological sequencing of events in the event sequence, and optionally selecting sequencing of events serving as a parent node in the causal network before events serving as child nodes;
visual layout: generating a horizontal axis of the events from left to right according to the occurrence sequence of the events, and then generating a vertical axis of the events from top to bottom according to the topological sorting of the events, wherein the positions on the coordinate axis are all marked with corresponding event names; generating a dot representing an event, the ordinate and the abscissa referring to the position of the event on the coordinate axis; connecting the event dots from left to right; for an event with a parent node in a causal subnetwork, a solid rectangle is placed beside a dot of the event, the solid rectangle is called an event rectangle, and the length of the rectangle represents the number of the parent nodes of the event; connecting the event round dots and the event rectangles in a stream mode to show the causal relationship in the causal subnetwork; adjusting the position of the event rectangle on the transverse axis in a force-guided manner according to the cause and effect structure, so as to ensure the readability of the cause and effect structure; in this visualization, the chronological order of the occurrence of the events can be seen from left to right, and the causal relationship of the occurrence of the events can be seen from top to bottom.
7. A causal visualization method oriented to an event sequence is applied to causal visualization of a table tennis motion event sequence and is characterized by comprising the following steps:
inputting a causal relationship between table tennis technologies and a table tennis technology sequence conforming to the causal relationship, and visually generating a layout about the causal relationship of the table tennis technology by using an existing causal network;
counting event information according to a ping-pong skill sequence, displaying the event information through icon visualization, representing event categories by using colors of icons, representing frequency by using radian of outer-layer rings, representing time distribution by using pie charts in the icons, and placing the generated event icons on corresponding event layouts in causal network visualization, wherein the event information comprises technology categories, time distribution of technology use and frequency of technology use;
excavating technology use patterns frequently appearing in a table tennis technology sequence by using a frequent pattern excavation algorithm, and visually excavating the obtained table tennis technology use patterns in a time line form;
for each table tennis technique use mode, generating a causal event stream according to the causal relationship of the events in the causal network, and showing the time development sequence of the table tennis technique and the causal relationship between the techniques;
a visualization of all ping-pong skill sequences is generated in the form of a timeline.
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